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Econometrics 2015, 3(2), 187-198; doi:10.3390/econometrics3020187

Information Recovery in a Dynamic Statistical Markov Model

1
Economics and Management of Agrobiotechnology Center, University of Missouri, Columbia, MO 65211, USA
2
Graduate School, 207 Giannini Hall, University of California, Berkeley, Berkeley, CA 94720, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Kerry Patterson
Received: 17 October 2014 / Accepted: 25 February 2015 / Published: 25 March 2015
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Abstract

Although economic processes and systems are in general simple in nature, the underlying dynamics are complicated and seldom understood. Recognizing this, in this paper we use a nonstationary-conditional Markov process model of observed aggregate data to learn about and recover causal influence information associated with the underlying dynamic micro-behavior. Estimating equations are used as a link to the data and to model the dynamic conditional Markov process. To recover the unknown transition probabilities, we use an information theoretic approach to model the data and derive a new class of conditional Markov models. A quadratic loss function is used as a basis for selecting the optimal member from the family of possible likelihood-entropy functional(s). The asymptotic properties of the resulting estimators are demonstrated, and a range of potential applications is discussed. View Full-Text
Keywords: conditional moment equations; controlled stochastic process; first-order Markov process; Cressie-Read power divergence criterion; quadratic loss; adaptive behavior conditional moment equations; controlled stochastic process; first-order Markov process; Cressie-Read power divergence criterion; quadratic loss; adaptive behavior
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Miller, D.J.; Judge, G. Information Recovery in a Dynamic Statistical Markov Model. Econometrics 2015, 3, 187-198.

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